Extended Input-Output Model

The used to analyze is an extension of the model of Input-Output developed by Leontief in the 1930s-1940s and has expanded considerably since then. The Leontief model is based on the interdependent relationship between one sector and another sector of the economy. The basic equation model of Leontief is as follows:

X shows the vector of total sectoral outputs. A shows the direct coefficient matrix. Y shows the column vector of total final demand. I shows the matrix of identity and (I – A)^–1 shows the matrix of Leontief inverse, representing direct impacts and indirect impacts due to a unit change in final demand. This shows the Type I multiplier, which is defined as (direct and indirect impacts)/(direct impact). This means that this Type I involves two impacts [16, 21-27]. Furthermore, Equation (1) can be expanded by multiplying it by the coefficient of gross value added (GVA'). Therefore, the value of sectoral GVA might be estimated. The equation for sectoral GVA type I is as follows:

GVA shows the vector of gross value added and GVA' shows the diagonal matrix of gva coefficients; gva is the coefficient of gross value added:

GVA coefficient of sector j: gvaj=GVAjXj

Likewise, Equation (1) can be extended to predict the labor required. It can be expanded by multiplying it by the labor coefficient, and then the sectoral labor can be predicted. The equation for sectoral labor of type I is as follows:

L is the vector of sectoral labor, L' is the diagonal matrix of the labor coefficient, l_j is labor coefficient:

Labor coefficient of sector j: lj=LjXj

Moreover, researchers related to LCA also use the extended equation model (1) to estimate the impacts on the environment, particularly GHGs due to the increase in final demand. The equation to estimate GHGs of type I is as follows:

GHGs is the vector of the emission, and GHGs' is the diagonal matrix of the emission coefficient (ghg) [15-18, 24].

Emission coefficient of sector j: ghgj=GHGsjXj

Furthermore, to identify the three impacts, i.e., direct impacts, indirect impacts, and induced effects due to the effect of household consumption, matrix A should be substituted by matrix A^*, informing that matrix A adds a column of the coefficient of household consumption and a row of wages and salaries coefficients coming from wages and salaries divided by total input. A^* is called a closed coefficient matrix, so the Equations (1), (2), (4), and (6) become as follows:

Where (I – A^*)^–1 shows the direct impacts, indirect impacts, and induced effects. Type II multipliers involve three impacts: direct, indirect, and household-spending effects (induced) of a final demand change. X ^* shows the output vector type II, GVA ^* shows gross value-added vector type II, L ^* shows the labor vector type II, and GHG ^* is the emission vector of GHGs type II [15, 16, 21-24].

Case Study

The region becoming a case study in this study is Daerah Istimewa (Special Region) Yogyakarta (DI Yogyakarta) for several reasons (Fig. 1). DI Yogyakarta is one of the 38 provinces in Indonesia that responded to net zero emission (NZE) by issuing DI Yogyakarta Governor’s Decree Number 51 of 2012 concerning the Regional Action Plan for Mitigating GHG Emissions. The commitment of the DI Yogyakarta Provincial Government to the development of the manufacturing industry sector that leads to the development of the Green Industry is regulated in DI Yogyakarta Regulation 7 of 2019 concerning the Plan of Industrial Development in Yogyakarta for 2019-2039. It is specifically stated in Article 3: implementing an independent industry with competitive and environmental perspectives. This shows the strong commitment of DI Yogyakarta local governments to develop green manufacturing industries. Furthermore, the role of the manufacturing industry sector in the regional economy, or Gross Regional Domestic Product of DI Yogyakarta, is relatively high compared to other sectors. Likewise, post Covid-19, the export value of DI Yogyakarta economic sectors showed a significant increase. In 2020, the export value of DI Yogyakarta Province was USD 398.6 million, in 2021 it was USD 557 million, and in 2022 it was USD 529 million [28-29], and it is projected that the export value in 2024 will be around USD 527 million (IDR 7.905 trillion).

The main data used was the DI Yogyakarta Input-Output Table for 52 sectors in 2016 and other supporting data issued by the Statistics Bureau of BPS, DI Yogyakarta Province, and the emission data was obtained from the Local Government DI Yogyakarta related to the Report of Greenhouse Gas Inventory [30, 31]. Some data was extrapolated, such as labor and emission data. This was done because there needs to be a sectoral data conversion whose classification is incompatible. All data collected is processed with Excel.

Fig. 1.

Case study of Daerah Istimewa (DI) Yogyakarta, Indonesia.

Overview Of DI Yogyakarta Region and Emissions

The region of DI Yogyakarta is a province in Indonesia located in the part of the island of Java. The Indonesian Ocean bounds the southern part of Yogyakarta, while the Northeast, Southeast, West, and Northwest bound the Central Java region. According to the Meteorology, Climatology, and Geophysics Agency (BMKG) records, the average air temperature in DI Yogyakarta in 2021 was 27.8ºC. Furthermore, the economy of DI Yogyakarta Province has also experienced an increase in growth. In the fourth quarter of 2022, the economic growth of DI Yogyakarta Province was 5.53% (year on year). The economic structure is dominated by the business fields of manufacturing, information and communication industries, agriculture, and construction, as well as the sectors of accommodation, food, and beverage, with a contribution of 52.01 percent. In 2020, the export value of DI Yogyakarta Province was USD 398.6 million, USD 557 million in 2021, and USD 529 million in 2022. It is projected that the export value in 2024 will be around USD 527 million, or around IDR 7.905 trillion. The manufacturing industry is an important sector for DI Yogyakarta. The average contribution of this sector to this GRDP is about 13 percent per year, making the manufacturing industry the first largest sector, followed by the information and communication industry [29- 32].

The Provincial Government of DI Yogyakarta has conducted a GHG inventory based on the National GHG Inventory Guidelines since 2012. The 2013 National GHG Inventory Report is the second GHG inventory conducted by the government. In general, this GHG inventory uses the ‘Tier’ method 1 to 2 in almost all categories. In some cases, especially in land use categories, inventory is conducted using Tier 3. The depth of the method varies between sectors and between categories due to different documentation and data quality. The equation for estimating GHG emissions and removals can be written in the form of the following simple equation: GHG Emissions = AD x EF, Where AD is activity data, namely data on development activities or human activities that produce GHG emissions or removals and EF is a GHG emission or absorption factor that shows the amount of emissions/ removals per unit of activity carried out. There are 4 aggregate sectors whose emissions are measured, namely: 1). Energy procurement & use; 2). Industrial process & product use; 3). Agriculture, forestry, & other land use; and 4). Waste management [31].

Total GHG emissions from all economic activities in DI Yogyakarta fluctuated in the period 2012-2020. The data shows that in 2012, GHG emissions amounted to 5,742.07 Gg CO₂e, then continued to increase until 2016 to 8,565.15 Gg CO₂e and in 2017, it increased dramatically to reach 12,378.06 Gg CO₂e. The increase in emissions was caused by changes in land cover (the forestry sector) of 5,946 Gg CO₂e. In 2018, GHG emissions decreased to 4,632.01 Gg CO₂e. From 2019 to 2020, there was a significant increase from 5,285.50 to 8,305.18 Gg CO₂e [31].

Calculation Results of The Impacts Type I with Two Impacts: Direct Impacts and Indirect Impacts

In this subsection, the direct and indirect impacts of the export increase of around IDR 7.905 trillion will be examined on creating output, GVA, absorbed workforce and CO₂e emissions from 14 manufacturing industry sectors in the DI Yogyakarta region, namely 1). Industry of Food & Beverage; 2). Industry of Tobacco; 3). Industry of Textile & Clothes; 4). Industry of Leather, Footwear & Leather Goods; 5). Industry of Wood, Cork, Rattan & Bamboo Goods; 6). Industry of Paper & Paper Goods, Reproduction of Recorded Media & Printing; 7). Industry of Chemical, Traditional Medicine & Pharmaceutical; 8). Industry of Rubber, Plastics & Rubber Goods; 9). Industry of Nonmetallic Excavated Goods; 10). Industrial Goods of Metals, Electronic Goods, Computer, Optics & Equipment of Electricity; 11). Industry of Machinery & Equipment; 12). Industry of Transportation Equipment; 13). Industry of Furniture; and 14). Industry of Other Processing, Installation Services & Repair of Machinery & Equipment.

Direct and indirect impacts on output creation, GVA, absorbed labour, and the number of carbon emissions incurred will be identified. The output is calculated based on the multiplication of the volume of goods and services produced by the price per unit. Gross Value Added or also referred to as Primary Input, is a cost paid to production factors such as labour, capital owners, fixed capital goods, and land. GVA consists of components: a). wages and salaries; b). business surplus, which is a combination of profits, land rent, and net capital interest payments (including net dividend payments); c). depreciation; d). indirect taxes; and e). subsidies on goods and services. In many macro analyses of labour, it is often referred to as employment opportunities or employment [30-31].

Table 1 shows the calculation results of the impacts Type I, direct and indirect impacts of increased exports on creating output and gross value added. The table shows that the output created by 14 manufacturing industries is IDR 5,124,899.37 million. The five largest sectors, namely Industry of Textile & Clothes, which amounted to almost IDR 2.59 trillion, were followed by Industry of Furniture of IDR 469,469.41 million; Industry of Leather, Footwear & Leather Goods of IDR 463,113.07 million; Industry of Food & Beverage of IDR 336,169.72 million; Industry of Wood, Cork, Rattan & Bamboo Goods amounting to IDR 271,222.89 million. Table 1 also shows that the gross value added generated by 14 manufacturing industry sectors is IDR 2,013,120.44 million. The five largest manufacturing sectors are the Industry of Textile & Clothes, which is almost IDR 975,898.59 million, followed by the Industry of Furniture of IDR 224,189.10 million, the Industry of Leather, Footwear & Leather Goods at IDR 192,655.14 million, the Industry of Food & Beverage at IDR 98,112.25 million, and the Industry of Wood, Cork, Rattan & Bamboo Goods amounting to IDR 119,498.86 million.

The results of calculation type I with two impacts on employment are shown in Fig. 2. The picture shows that the total workforce absorbed by 14 manufacturing industry sectors is 65,340.

The five largest sectors are: Industry of Textile & Clothes, with as many as 32,986 people, or almost 50% of the total; Industry of Furniture, with as many as 5,985 people; Industry of Leather, Footwear & Leather Goods of 5,904 people; Industry of Food & Beverage of 4,286 people; and Industry of Wood, Cork, Rattan & Bamboo Goods, as many as 3,457 people. However, the calculation results also show that the impact on carbon emissions is reflected in Fig. 3. The figure shows that the total carbon emissions produced by 14 manufacturing industry, either directly or indirectly, amounted to 35.26 GgCO₂e. The five largest sectors are namely the Industry of Textile & Clothes of 18.26 GgCO₂e, which is the highest one, the Industry of Leather, Footwear & Leather Goods of 3.06 Gg CO₂e, the Industry of Furniture of 2.78 GgCO₂e, Industry of Food & Beverage of 2.70 GgCO₂e and Industry of Wood, Cork, Rattan & Bamboo Goods of 1.72 GgCO₂e.

Fig. 2.

Impacts Type I of exports on employment.

Fig. 3.

Impacts Type I of exports on carbon CO₂e emissions.

In this sub-section, it can be summarized that an increase in exports of IDR 7.905 trillion from the DI Yogyakarta region will, directly and indirectly, impact 14 manufacturing industry sectors in the creation of output of IDR 5,124,899.37 million, gross value added of IDR 2,013,120.44 million, and a workforce of as many as 65,340 people.

However, at the same time, CO₂e emissions of 35.26 GgCO₂e will arise. The five manufacturing industry sectors producing the most such values are Industry of Textile & Clothes; Industry of Furniture; Industry of Leather, Footwear & Leather Goods; Industry of Food & Beverage; and Industry of Wood, Cork, Rattan & Bamboo Goods.

A pattern reflects a unidirectional correlation between output values and emerging carbon emissions (Fig. 4). The greater the output produced by the manufacturing industry sector, the greater the emissions it causes. This might be because the greater the output produced, the greater the energy used in the production process, so that CO₂e emissions are even greater. This pattern seems to be compatible with previous researchers’ arguments that there is a positive correlation between the growth of the economy and greenhouse gas emissions; the higher the growth of the economy, the greater the carbon emissions, especially in developing countries.

Calculation Results Of Type II with 3 Impacts:

Direct Impacts, Indirect Impacts and Induced Effects

This subsection will examine the impact of increasing exports projected in 2024 at IDR 7.905 trillion, and in this calculation of type II, we will calculate three impacts, namely direct impacts, indirect impacts, and induced effects, on 14 manufacturing industry sectors in the DI Yogyakarta region. Table 2 shows the calculation results of such three impacts; it can be seen that the creation of output from 14 manufacturing industry sectors is IDR 5,578,178.45 million. The 5 largest sectors are namely the Industry of Textile & Clothes of IDR 2,615,123.04 million, which will be followed by the Industry of Food & Beverage of IDR 690,785.34 million; the Industry of Leather, Footwear & Leather Goods of IDR 480,811.96 million; the Industry of Furniture of IDR 479,998.30 million; and the Industry of Wood, Cork, Rattan & Bamboo Goods of IDR 273,998.49 million. Table 2 also shows the direct, indirect, and induced impacts due to an increase in exports on the gross added value that the industry will receive. The table shows that the gross value-added generated from 14 manufacturing industry sectors is IDR 2,161,252.07 million. The 5 largest manufacturing industry sectors are namely the Industry of Textile & Clothes at IDR 986,330.29 million, followed by the Industry of Furniture of IDR 229,217.04; the Industry of Food & Beverage at IDR 201,608.00 million; the Industry of Leather, Footwear & Leather Goods at IDR 200,017.88 million; and the Industry of Wood, Cork, Rattan & Bamboo Goods at IDR 120,721.77 million.

Fig. 4.

Patterns of relationship between output and carbon emissions Type I.

Fig. 5.

Impacts Type II of exports on employment.

Fig. 6.

Impacts Type II of exports on carbon CO₂e emissions.

The results of calculation type II with three impacts on the workforce are shown in Fig. 5. The picture shows that the total workforce absorbed by 14 manufacturing industry sectors is 71,119. The five largest sectors are: the Industry of Textile & Clothes, as many as 33,339 people or almost 50% of the total; the Industry of Food & Beverage of 8,807 people; the Industry of Leather, Footwear & Leather Goods of 6,130 people; Industry of Furniture, as many as 6,119 people; and Industry of Wood, Cork, Rattan & Bamboo Goods, as many as 3,493 people.

However, the calculation results also show that the impact on carbon emissions is reflected in Fig. 6. The figure shows that the total carbon emissions produced by 14 manufacturing industries, either directly or indirectly, as well as induced effects, amounted to 38.72 GgCO₂e. The five largest sectors are namely: Industry of Textile & Clothes of 18.46 GgCO₂e, which is the highest one; Industry of Food & Beverage (5.54 GgCO₂e); Industry of Leather, Footwear & Leather Goods (3.18 GgCO₂e); Industry of Furniture (2.84 GgCO₂e); and Industry of Wood, Cork, Rattan & Bamboo Goods (1.74 GgCO₂e).

The Covid-19 pandemic has badly impacted health and the socio-economy [33]. However, as mentioned before, the post Covid 19 export value of DI Yogyakarta economic sectors showed a significant increase. The export value in 2024 is projected to be around USD 527 million, or around IDR 7.905 trillion. An increase in exports of IDR 7.905 trillion from the DI Yogyakarta region will cause three impacts, namely direct impact, indirect impact, and induced effect (Type II) on 14 manufacturing industry sectors in the creation of output of IDR 5,578,178.45 million, gross value added of IDR 2,161,252.07 million, and a workforce that might absorb as many as 71,119 people. However, at the same time, CO₂e emissions of 38.72 GgCO₂e will arise. The five manufacturing industry sectors producing the most such values are Industry of Textile & Clothes; Industry of Furniture; Industry of Leather, Footwear & Leather Goods; Industry of Food & Beverage; and Industry of Wood, Cork, Rattan & Bamboo Goods.

Fig. 7.

Patterns of relationship between output and carbon emissions Type II.

Likewise, for this calculation of type II, again, there is a pattern reflecting a unidirectional correlation between economic output and emerging carbon emissions. This result emphasizes a correlation between economic output and emissions of CO₂e (see Fig. 7). This confirms the argument of some researchers that the growth of the economy has a positive relationship with industrialization and the emission of carbon, particularly in developing countries [1-2, 6, 8]. These results are also compatible with other scholars’ research showing that the development of economies has positive correlations with the incidence of greenhouse gases, albeit with different methodologies.

Mitigation of carbon emissions through the diffusion of clean technology innovations in the processing industry is very important. Technology development, including clean technology, might be produced by a conducive national system of innovation [34]. Therefore, the development of green industries, in parallel, must also be developed clean and efficient technology by strengthening the national innovation system. Tax policies might also be implemented to compel the manufacturing industries to adopt clean technology.

Another strategy that might be done in parallel with reducing greenhouse gas emissions is to use the principle of Low Carbon Development (LCD) for the physical development of the industry. Planning items such as building design, land cover, and water and waste management systems have implemented the principles of LCD [35].

Limitation

There are likely two things that cause this study to have limitations: limitations sourced from the data collected and assumptions from the input-output model itself, which still have pros and cons. The sector classification of emissions issued by the Provincial Government of DI Yogyakarta differs from the sectors’ classification in the input-output table. Therefore, it is necessary to first extrapolate the classification of emission data released by the DI Yogyakarta government to be equated with the classification of sectors in the input-output table. This might create uncertainty. Likewise, the workforce and projected data on exports of extrapolated goods and services also have uncertainty. Some assumptions in the output-input model might be a source of weakness. One of the assumptions of the input-output model is a constant return to scale, or that the relationship between output and input is linear. This means that each input unit into the production process will produce units of output linearly, when in reality, they are non-linear and cannot be substituted. Perhaps the implications of uncertainty are more complex than that [22, 24]. Thus, the interpretation of the results of the analysis must be done in the context of uncertainty. However, the benefits of this extended input-output model are still quite powerful as an analytical method that can calculate economic and environmental impacts in an integrated and comprehensive manner for regional economic development planning by considering the environment. In addition, the output-input table data and greenhouse gas inventory data are regularly issued by BPS-Statistics and the government. Therefore, this assessment can be evaluated regularly and easily.